* M.Tech.(Research Scholar (CSE)), Email: [email protected]
** Associate Professor ((CSE)-ACET, Amritsar)), Email: [email protected]
Comparative Analysis of MachineLearning Algorithms for ChronicKidney Disease Detection using WekaMilandeep Arora* and Ajay Sharma**
ABSTRACT
Chronic kidney disease, a dangerous and life threatening disease which is very fatal and common now a days. Wehave worked to control and detect this disease very minutely in this paper by comparing the results of variousoutcomes of different algorithms used here. Three algorithms have been used in this paper i.e. Naïve bayes, AD treeand LWL, all are of different classifier groups. The comparisons have been made by testing the results of thesethree algorithms in Explorer and Experimenter interfaces of WEKA data mining tool based on four parameters i.e.number of instances either correctly or incorrectly classified, ROC area, mean absolute error and classified accuracy.In the end after all the comparisons and analysis, it has been found that AD tree is the best analysis classifieralgorithm for detecting chronic kidney disease(CKD).
Index Terms: Chronic kidney disease(CKD), WEKA, classification algorithms, etc.
1. INTRODUCTION
In this paper, we have used medical datasets of chronic kidney disease readily available on UCI (universityof California) repository [1] and made them introduce to WEKA data mining tool [2] with different algorithmsas mentioned above. We have used two different interfaces in this paper to compare the results. Varioussymptoms of chronic kidney have been used in this paper to study the comparison of different algorithms.The main aim of this paper is to make acute comparative analysis of chronic kidney disease and to knowwhich algorithm turns out to be the best in analyzing the disease. And if we want to number the objectivesof this paper, it can be as follows
1. To analyze the results of chronic kidney disease medical datasets in WEKA.
2. Compare the results in Explorer and Experimenter interface with various parameters.
After that the paper follows this procedure i.e. section two tells about the details of symptoms of chronickidney disease, section three tells about the medical datasets used in this paper, section four lets you knowabout the literature survey that is being used to design this paper, section five tells us about the methodologyused in this paper, results are displayed and compared in section six and conclusion and future scope is givenin the section seven. In the end, the references are given from whom the thought and concept of this paper iscarried out and without their support and help, this research wouldn’t have been the same or as effective.
2. SYMPTOMS OF CHRONIC KIDNEY DISEASE
We have used 24 symptoms of chronic kidney disease which are considered while detecting this diseaseand they are as follows
ISSN: 0974-5572I J C T A, 10(8), 2017, pp. 59-67© International Science Press
60 Milandeep Arora and Ajay Sharma
Table 1List of Symptoms Used to Detect Chronic Kidney Disease
S. No. Symptom Short Form
1. Age Age
2. Blood pressure Bp
3. Specific gravity Sg
4. Albumin Al
5. Sugar Su
6. Red blood cells Rbc
7. Pus cell Pc
8. Pus cell clumps pcc
9. Bacteria ba
10. Blood glucose random bgr
11. Blood urea bu
12. Serum creatinine sc
13. Sodium sod
14. Potassium pot
15. Hemoglobin hemo
16. Packed cell volume pcv
17. White blood cell count wc
18. Red blood cell count rc
19. Hypertension htn
20. Diabetes mellitus dm
21. Coronary artery disease cad
22. Appetite appet
23. Pedal edema pe
24. Anemia ane
25. Class class
The symptoms used in this paper for detecting chronic kidney disease are given above in Table 1 withtheir names and short forms which are used in this paper [3].
3. MEDICAL DATASETS
Dataset is a collection of data or a single statistical data where every attribute of data represents variableand each instance has its own description. For the prediction of Chronic kidney disease, we have usedmedical datasets [4] in order to compare their accuracy using wekas Explorer and Experimenter interface.The datasets used by us contains 25 attributes and 400 instances out of which 250 are suffering from thedisease and 150 are not suffering from the disease. We have applied different algorithms using WEKA datamining tool for our analysis purpose.
4. LITERATURE SURVEY
Naganna chetty et al [6] has built classification models with different classification algorithms i.e. wrappersubset attribute evaluator and best first search method to predict and classify the CKD and non CKDpatients. The models have been applied to medical datasets and it has been concluded that classifiers hasperformed better on reduced datasets than the original ones.
Lambodar jena et al [7] has suggested the use of six classifiers present in weka data mining tool andthen studied their performance based on various parameters.
Comparative Analysis of Machine Learning Algorithms for Chronic Kidney Disease Detection... 61
Milandeep et al [8] in his paper tells about the complete details of chronic kidney disease, its symptomsand the datasets that were helpful in predicting this disease effectively.
S.Ramya et al [9] in his paper has performed experiments on chronic kidney disease datasets withvarious algorithms such as back propagation neural network, radial basis function and random forest and ithas been found that radial basis function algorithm performs the best out of three.
Parul sinha et al [10] has compared the performance of results performed on CKD datasets based onSVM and KNN classifiers and it has been found that KNN is better than SVM.
Dhamodran et al [11] has done prediction of liver disease using naïve bayes and functional tree algorithmsand concluded that naïve bayes algorithm is best predicting this disease.
N k kameswara rao et al [12] has tried to discover the fast, easy and efficient data mining algorithm inprediction of epidemic disease with minimum errors, having large datasets and show reasonable patternswith dependent variables.
5. METHODOLOGY
The above Figure 1 shows the methodology used in this paper or the flow of work done accordingly, firstthe data is been searched from various sources available and then it is integrated to one suitable form i.e.ARFF and .CSV formats. After that it is being introduced to WEKA data mining tool on two interfaces i.e.Explorer and Experimenter and in the end the results are carried out and compared using suitable tables.We have used three different algorithms for this purpose i.e. naïve bayes, AD (alternating decision) tree and
Figure 1: Showing Flow of Methodology
62 Milandeep Arora and Ajay Sharma
LWL (locally weighted learning). In order to carry out experimentations and implementations Weka wasused as the data mining tool. Weka (Waikato Environment for Knowledge Analysis) is a data mining toolwritten in java developed at Waikato. WEKA is an excellent data mining tool for the users to classify theaccuracy on the basis of datasets by applying different algorithmic approaches and compared in the field ofbioinformatics [13]. In data mining tools classification deals with identifying the problem by observingcharacteristics of diseases amongst patients and diagnose or predict which algorithm shows best performanceon the basis of WEKA’s statistical output [14] Table 2 shows the WEKA data mining techniques that havebeen used in this paper along with other prerequisites like data set format etc. by using different algorithms.
The interfaces we have used in this paper are Explorer and Experimenter. In this study we classified theaccuracy of different algorithms Naïve bayes, AD tree and LWL on different datasets and compared theresults to know which algorithm shows best performance. All the algorithms used by us were applied to achronic kidney disease are from different types i.e. naive bayes is from bayes classifier, LWL is from lazyclassifier and AD tree is from tree classifier. In order to obtain better accuracy 10 fold cross validation wasperformed. For each classification we selected training and testing sample randomly from the base set totrain the model and then test it in order to estimate the classification and accuracy measure for each classifier.The parameters used by us are:
1. Number of instances i.e. 400Either correctly classified or incorrectly classified dependent on algorithm used
2. ROC (receiver operating characteristic) area
3. Mean absolute error
4. Classified accuracy
6. RESULTS AND COMPARISONS
6.1. Explorer Interface
6.1.1. Naïve Bayes
In Figure 2 classification accuracy achieved is 95% out of total 400 instances in which there are 380correctly classified instances and 20 are not correctly classified, mean absolute error is 0.0479 and ROCarea is 1.
6.1.2. AD(Alternating Decision) Tree
In Figure 3 classification accuracy achieved is 99.75% out of total 400 instances in which there are 399correctly classified instances and 1 is not correctly classified, mean absolute error is 0.0203 and ROC areais 1.
6.1.3. LWL(Locally Weighted Learning)
In Figure 4 classification accuracy achieved is 92.25% out of total 400 instances in which there are 369correctly classified instances and 31 are not correctly classified, mean absolute error is 0.1132 and ROCarea is 0.994.
Table 2Showing data mining techniques
Software WEKA Interface Classification algorithms Purpose
WEKA Explorer Naïve bayes, AD tree, LWL Analyzing and Comparison
Experimenter Naïve bayes, AD tree, LWL Analyzing and Comparison
Comparative Analysis of Machine Learning Algorithms for Chronic Kidney Disease Detection... 63
Figure 2: Results of Naive Bayes Algorithm
Figure 3: Results of AD Tree Algorithm
64 Milandeep Arora and Ajay Sharma
6.2. Experimenter Interface
6.2.1. Naive Bayes
In Figure 5 classification accuracy achieved is 95.20% out of total 400 instances in which there are 381 correctlyclassified instances and 19 are not correctly classified, mean absolute error is 0.05 and ROC area is 1.
Figure 4: Results of LWL Algorithm
Figure 5: Results of Naive Bayes Algorithm
Comparative Analysis of Machine Learning Algorithms for Chronic Kidney Disease Detection... 65
6.2.2. AD(Alternating Decision) Tree
In Figure 6 classification accuracy achieved is 99.58% out of total 400 instances in which there are 398correctly classified instances and 2 are not correctly classified, mean absolute error is 0.02 and ROC area is 1.
6.2.3. LWL(locally weighted learning)
In Figure 7 classification accuracy achieved is 92.28% out of total 400 instances in which there are 369 correctlyclassified instances and 31 are not correctly classified, mean absolute error is 0.11 and ROC area is 1.
Figure 7: Results Of LWL Algorithm
Figure 6: Results Of AD Tree Algorithm
66 Milandeep Arora and Ajay Sharma
Table 3Result Of Explorer Interface
Explorer
No. of Instances ROC MAE Classified Accuracy
400
Naïve Bayes C IC 1 0.0479 95%
380 20
400
AD TREE C IC 1 0.0203 99.75%
399 1
400
LWL C IC 0.994 0.1132 92.25%
369 31
Table 4Result Of Experimenter Interface
Explorer
No. of Instances ROC MAE Classified Accuracy
400
NAÏVE BAYES C IC 1 0.05 95.20%
381 19
400
AD TREE C IC 1 0.02 99.58%
398 2
400
LWL C IC 1 0.11 92.28%
369 31
The above Table 3 and Table 4 shows the comparison of results of two interfaces i.e. Explorer andExperimenter of weka data mining tool between three algorithms i.e. naive bayes, AD tree and LWL. Theparameters used in this comparison are Number of instances either correctly classified(C) and incorrectlyclassified(IC), ROC(receiver operating characteristic) area, MAE(mean absolute error) and classifiedaccuracy. The above comparison shows that there is very minute difference between the results of Explorerand Experimenter interface of weka data mining tool and from readings from both the interfaces, it isclearly visible that, LWL i.e. locally weighted learning algorithm outperforms other algorithms and henceis the best in analysing and detecting chronic kidney disease. We have used both these interfaces becausethere is a slight difference between there results and we haven’t used the third interface that is Knowledgeflow because of two reasons i.e. first it is an alternate method to Explorer interface and secondly we haveused it in our earlier paper i.e. Chronic kidney disease detection by analysing medical datasets in weka [15]which was published in International journal of computer applications in august 2016 edition.
7. CONCLUSION AND FUTURE SCOPE
The main aim of this paper is to compare the results of three different algorithms of different class and it isbeing justified by using naïve bayes algorithm which belongs to bayes class, AD tree algorithm whichbelongs to tree class and LWL algorithm which belongs to lazy class. After performing all the experiments,
�����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������
